Ee Yeo Keat | Searcharxiv

arXiv2026

Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answering

In this work we present Knowledge Module Learning (KML) to understand and reason over procedural tasks that requires models to learn structured and compositional procedural knowledge. KML is a neurosymbolic framework that learns relation categories within a knowledge graph as neural knowledge modules and composes them into executable reasoning programs generated by large language models (LLMs). Each module encodes a specific procedural relation capturing how each entity type such as tools are related to steps, purpose of each tool, and steps of each task. Given a question conditioned on a task shown in a video, then KML performs multistep reasoning with transparent, traceable intermediate states. Our theoretical analysis demonstrated two desired properties of KML. KML satisfy strong optimal conditions for modelling KG relations as neural mappings, providing strong foundations for generalizable procedural reasoning. It also shows a bound on the expected error when it performs multistep reasoning. To evaluate this model, we construct a large procedural knowledge graph (PKG) consisting of diverse instructional domains by integrating the COIN instructional video dataset, and COIN ontology, commonsense relations from ConceptNet, and structured extractions from LLMs, followed by expert verification. We then generate question and answer pairs by applying graph traversal templates over the PKG, constructing the PKR-QA benchmark for procedural knowledge reasoning. Experiments show that KML improves structured reasoning performance while providing interpretable step-by-step traces, outperforming LLM-only and black-box neural baselines. Code is publicly available at https://github.com/LUNAProject22/KML.

cs.CV↗

arXiv2024

Training-Free Action Recognition and Goal Inference with Dynamic Frame Selection

We introduce VidTFS, a Training-free, open-vocabulary video goal and action inference framework that combines the frozen vision foundational model (VFM) and large language model (LLM) with a novel dynamic Frame Selection module. Our experiments demonstrate that the proposed frame selection module improves the performance of the framework significantly. We validate the performance of the proposed VidTFS on four widely used video datasets, including CrossTask, COIN, UCF101, and ActivityNet, covering goal inference and action recognition tasks under open-vocabulary settings without requiring any training or fine-tuning. The results show that VidTFS outperforms pretrained and instruction-tuned multimodal language models that directly stack LLM and VFM for downstream video inference tasks. Our VidTFS with its adaptability shows the future potential for generalizing to new training-free video inference tasks.

cs.CV↗